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chore(config): disable embeddings by default and update documentation (#341)

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- Set default version to 0.4.1.0
- Comment out embedding configuration in default.yaml
- Update README and README_ZH to clarify embedding components are disabled by default
- Add note explaining how to enable embedding-based semantic retrieval
- Adjust table formatting and descriptions in documentation
- Modify search command description to reflect vector search availability when enabled
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67
README.md
View file

@ -58,7 +58,9 @@ memory, then continuously indexes, links, and consolidates that memory for futur
## 📰 News
- [2026.07] - Our paper [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/) has been accepted to Findings of ACL 2026.
- [2026.07] - Our
paper [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/)
has been accepted to Findings of ACL 2026.
## 🚀 Quick Start
@ -82,13 +84,14 @@ pip install -e ".[core]"
### Environment Variables
Configure environment variables when you want LLM-powered memory evolution or embedding retrieval:
Configure environment variables when you want LLM-powered memory evolution or embedding retrieval. Embeddings are
disabled by default, so the default setup does not start an embedding model or require an embedding API key.
```bash
cat > .env <<'EOF'
# Optional: enables semantic retrieval when the embedding store is configured.
EMBEDDING_API_KEY=sk-xxx
EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
# Optional: used only after embedding components are explicitly enabled in the config.
# EMBEDDING_API_KEY=sk-xxx
# EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
# Required for auto_memory, auto_resource, and auto_dream.
LLM_API_KEY=sk-xxx
@ -98,6 +101,12 @@ EOF
Basic file operations, BM25 search, wikilink traversal, and reading proactive topics can run without LLM credentials.
> [!NOTE]
> To enable embedding-based semantic retrieval, uncomment `components.as_embedding` and
> `components.embedding_store` in [`reme/config/default.yaml`](reme/config/default.yaml), then change
> `components.file_store.default.embedding_store` from `""` to `default`. See the
> [memory search guide](docs/en/memory_search.md) for details.
### Start the Service
```bash
@ -193,7 +202,8 @@ ReMe treats **memory as files**, progressively processing raw conversations and
## 🧭 Memory Design Philosophy
> Capture raw dialogs and resources, refine them into long-term preferences, reusable experience, and valuable knowledge,
> Capture raw dialogs and resources, refine them into long-term preferences, reusable experience, and valuable
> knowledge,
> while keeping the result editable by humans and agents.
### Automatic Memory Flow
@ -202,13 +212,13 @@ ReMe follows a capture → index → consolidate → recall loop. Conversations
background jobs keep files searchable; `auto_dream` distills stable knowledge into `digest/`; agents recall memory
through search, wikilinks, or proactive topics.
| Capability | Entry point | What it does | Output |
|---------------------------------------------|--------------------------------------------------|-----------------------------------------------------------------------------------------------|----------------------------------------------------------|
| [`auto_memory`](docs/en/auto_memory.md) | Agent hook or `reme auto_memory` | Distills useful conversation facts while preserving the raw session. | `session/dialog/*.jsonl`, `daily/<date>/<session>.md` |
| [`auto_resource`](docs/en/auto_resource.md) | Resource watcher or `reme auto_resource` | Turns files under `resource/<date>/` into source-linked daily cards. | `daily/<date>/<resource-card>.md` |
| [`auto_index`](docs/en/memory_search.md) | Background watcher or `reme reindex` | Maintains chunks, BM25/embedding indexes, and the wikilink graph. | Searchable `daily/`, `digest/`, and `resource/` content |
| [`auto_dream`](docs/en/auto_dream.md) | `dream_cron` or `reme auto_dream` | Consolidates changed daily cards into long-term personal, procedure, and wiki memory. | `digest/**`, `daily/<date>/interests.yaml` |
| [`proactive`](docs/en/proactive.md) | `reme proactive` before an agent decides to act | Reads topics generated by `auto_dream`; the host agent decides whether and how to mention them. | Structured topics from `daily/<date>/interests.yaml` |
| Capability | Entry point | What it does | Output |
|---------------------------------------------|-------------------------------------------------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------|
| [`auto_memory`](docs/en/auto_memory.md) | Agent hook or `reme auto_memory` | Distills useful conversation facts while preserving the raw session. | `session/dialog/*.jsonl`, `daily/<date>/<session>.md` |
| [`auto_resource`](docs/en/auto_resource.md) | Resource watcher or `reme auto_resource` | Turns files under `resource/<date>/` into source-linked daily cards. | `daily/<date>/<resource-card>.md` |
| [`auto_index`](docs/en/memory_search.md) | Background watcher or `reme reindex` | Maintains chunks, the BM25 index, the wikilink graph, and the optional embedding index. | Searchable `daily/`, `digest/`, and `resource/` content |
| [`auto_dream`](docs/en/auto_dream.md) | `dream_cron` or `reme auto_dream` | Consolidates changed daily cards into long-term personal, procedure, and wiki memory. | `digest/**`, `daily/<date>/interests.yaml` |
| [`proactive`](docs/en/proactive.md) | `reme proactive` before an agent decides to act | Reads topics generated by `auto_dream`; the host agent decides whether and how to mention them. | Structured topics from `daily/<date>/interests.yaml` |
<table>
<tr>
@ -234,11 +244,11 @@ through search, wikilinks, or proactive topics.
ReMe runs as a local memory service and offers multiple integration paths: CLI, HTTP API, MCP server, and SDK. Different
agents can choose the path that fits their runtime while sharing the same local memory workspace.
| Agents | Recommended path | What works out of the box |
|------------------------------------------------------|-----------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
| **QwenPaw** | Embed ReMe via the Python SDK. | Reuse the app's own lifecycle and model config while keeping memory local and file-based. |
| **Claude Code** | Start ReMe as an MCP service and install [plugins/reme](plugins/reme). | MCP recall tools, a `reme-memory` skill, and a Stop hook that records sessions automatically. |
| **Other CLI-capable agents (OpenClaw/Hermes/Codex)** | Copy or install [skills/reme_memory/SKILL.md](skills/reme_memory/SKILL.md). | Search/read/write memory and call `auto_memory`, `auto_dream`, and `proactive` via the CLI. |
| Agents | Recommended path | What works out of the box |
|------------------------------------------------------|-----------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
| **QwenPaw** | Embed ReMe via the Python SDK. | Reuse the app's own lifecycle and model config while keeping memory local and file-based. |
| **Claude Code** | Start ReMe as an MCP service and install [plugins/reme](plugins/reme). | MCP recall tools, a `reme-memory` skill, and a Stop hook that records sessions automatically. |
| **Other CLI-capable agents (OpenClaw/Hermes/Codex)** | Copy or install [skills/reme_memory/SKILL.md](skills/reme_memory/SKILL.md). | Search/read/write memory and call `auto_memory`, `auto_dream`, and `proactive` via the CLI. |
<p align="center"><b>Integration demos</b></p>
@ -274,22 +284,23 @@ ReMe operates the workspace through a unified job interface exposed by the CLI.
reading, writing, editing, and automatic memory commands. Lower-level indexing, frontmatter, and file operation commands
are mainly for maintenance, debugging, or advanced integration. Run `reme help` for the full job list.
| Command | Purpose |
|-------------------------------------------|--------------------------------------------------------------------------------------|
| `reme start` | Start the local ReMe service. |
| `reme version` / `reme health_check` | Check package and component status. |
| [`reme search`](docs/en/memory_search.md) | Retrieve memory with hybrid search. |
| `reme read` / `reme write` / `reme edit` | Inspect and maintain Markdown memory files. |
| `reme auto_memory` | Turn conversation messages into daily memory cards. Requires LLM credentials. |
| Command | Purpose |
|-------------------------------------------|----------------------------------------------------------------------------------------|
| `reme start` | Start the local ReMe service. |
| `reme version` / `reme health_check` | Check package and component status. |
| [`reme search`](docs/en/memory_search.md) | Retrieve memory with BM25 and wikilinks by default, plus vectors when enabled. |
| `reme read` / `reme write` / `reme edit` | Inspect and maintain Markdown memory files. |
| `reme auto_memory` | Turn conversation messages into daily memory cards. Requires LLM credentials. |
| `reme auto_resource` | Interpret files under `resource/` into daily resource cards. Requires LLM credentials. |
| `reme auto_dream` / `reme proactive` | Consolidate daily memory into long-term digest and surface topics worth attention. |
| `reme reindex` | Rebuild search and wikilink indexes from existing files. |
| `reme auto_dream` / `reme proactive` | Consolidate daily memory into long-term digest and surface topics worth attention. |
| `reme reindex` | Rebuild search and wikilink indexes from existing files. |
## 🤝 Community and Support
- **Issues and requests**: Check [Open Issues](https://github.com/agentscope-ai/ReMe/issues) first. If there is no
related discussion, open a new issue with background, expected behavior, and impact scope.
- **Code contributions**: Before making changes, read the [contribution guide](https://docs.agentscope.io/reme/stable/en/contributing). Source,
- **Code contributions**: Before making changes, read
the [contribution guide](https://docs.agentscope.io/reme/stable/en/contributing). Source,
schemas, and tests are the authoritative architecture and extension guide.
- **Documentation contributions**: Submit user-facing documentation changes to the
[unified documentation repository](https://github.com/agentscope-ai/docs) under `reme/<version>/{en,zh}/`.

68
README_ZH.md
View file

@ -26,7 +26,8 @@
> [0.2.x](https://github.com/agentscope-ai/ReMe/tree/v0.2.0.6) ·
> [MemoryScope](https://github.com/agentscope-ai/ReMe/tree/memoryscope_branch)
🧠 ReMe 是一个面向 **AI 智能体** 的 local-first 记忆层。它把对话和资料沉淀为文件化长期记忆，并持续完成索引、链接和整理，让后续 Agent 能够可靠召回。
🧠 ReMe 是一个面向 **AI 智能体** 的 local-first 记忆层。它把对话和资料沉淀为文件化长期记忆，并持续完成索引、链接和整理，让后续
Agent 能够可靠召回。
## ✨ 核心创新
@ -50,8 +51,9 @@
## 📰 新闻
- [2026.07] - 我们的论文 [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/)
已被 Findings of ACL 2026 接收。
- [2026.07] -
我们的论文 [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/)
已被 Findings of ACL 2026 接收。
## 🚀 快速开始
@ -75,13 +77,14 @@ pip install -e ".[core]"
### 环境变量
如果需要 LLM 驱动的记忆演化或 embedding 检索，可以配置环境变量：
如果需要 LLM 驱动的记忆演化或 embedding 检索，可以配置环境变量。embedding 默认关闭，因此默认配置不会启动
embedding 模型，也不需要 embedding API key。
```bash
cat > .env <<'EOF'
# 可选：配置 embedding store 后启用语义检索。
EMBEDDING_API_KEY=sk-xxx
EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
# 可选：仅在配置中显式启用 embedding 组件后使用。
# EMBEDDING_API_KEY=sk-xxx
# EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
# 必须：auto_memory、auto_resource 和 auto_dream 需要 LLM。
LLM_API_KEY=sk-xxx
@ -91,6 +94,12 @@ EOF
基础文件读写、BM25 检索、wikilink 遍历和 proactive topics 读取可以先不配置 LLM 凭证。
> [!NOTE]
> 如需启用基于 embedding 的语义检索，请取消 [`reme/config/default.yaml`](reme/config/default.yaml) 中
> `components.as_embedding` 和 `components.embedding_store` 的注释，并将
> `components.file_store.default.embedding_store` 从 `""` 改为 `default`。完整说明见
> [记忆检索文档](docs/zh/memory_search.md)。
### 启动服务
```bash
@ -193,13 +202,13 @@ ReMe 将**记忆视为文件**，让原始对话和外部资料从 `session/`、
ReMe 遵循 capture → index → consolidate → recall 的循环。对话和资料先变成 daily 记忆卡片；后台任务保持文件可检索；
`auto_dream` 将稳定知识沉淀到 `digest/`；Agent 再通过搜索、wikilink 或 proactive topics 召回记忆。
| 能力 | 入口 | 作用 | 输出 |
|---------------------------------------------|-----------------------------------|-------------------------------------|----------------------------------------------------------|
| [`auto_memory`](docs/zh/auto_memory.md) | Agent hook 或 `reme auto_memory` | 提炼有长期价值的对话事实，同时保留原始 session。 | `session/dialog/*.jsonl`、`daily/<date>/<session>.md` |
| [`auto_resource`](docs/zh/auto_resource.md) | 资源监听或 `reme auto_resource` | 将 `resource/<date>/` 下的文件转为带来源链接的 daily 卡片。 | `daily/<date>/<resource-card>.md` |
| [`auto_index`](docs/zh/memory_search.md) | 后台监听或 `reme reindex` | 维护 chunks、BM25/embedding 索引和 wikilink 图谱。 | 可检索的 `daily/`、`digest/`、`resource/` 内容 |
| [`auto_dream`](docs/zh/auto_dream.md) | `dream_cron` 或 `reme auto_dream` | 将变化的 daily 卡片整理为长期 personal、procedure 和 wiki 记忆。 | `digest/**`、`daily/<date>/interests.yaml` |
| [`proactive`](docs/zh/proactive.md) | Agent 决定主动行动前调用 `reme proactive` | 读取 `auto_dream` 生成的 topics；是否以及如何提醒用户由宿主 Agent 决定。 | 来自 `daily/<date>/interests.yaml` 的结构化 topics |
| 能力 | 入口 | 作用 | 输出 |
|---------------------------------------------|----------------------------------|----------------------------------------------------|------------------------------------------------------|
| [`auto_memory`](docs/zh/auto_memory.md) | Agent hook 或 `reme auto_memory` | 提炼有长期价值的对话事实，同时保留原始 session。 | `session/dialog/*.jsonl`、`daily/<date>/<session>.md` |
| [`auto_resource`](docs/zh/auto_resource.md) | 资源监听或 `reme auto_resource` | 将 `resource/<date>/` 下的文件转为带来源链接的 daily 卡片。 | `daily/<date>/<resource-card>.md` |
| [`auto_index`](docs/zh/memory_search.md) | 后台监听或 `reme reindex` | 维护 chunks、BM25 索引、wikilink 图谱及可选的 embedding 索引。 | 可检索的 `daily/`、`digest/`、`resource/` 内容 |
| [`auto_dream`](docs/zh/auto_dream.md) | `dream_cron` 或 `reme auto_dream` | 将变化的 daily 卡片整理为长期 personal、procedure 和 wiki 记忆。 | `digest/**`、`daily/<date>/interests.yaml` |
| [`proactive`](docs/zh/proactive.md) | Agent 决定主动行动前调用 `reme proactive` | 读取 `auto_dream` 生成的 topics；是否以及如何提醒用户由宿主 Agent 决定。 | 来自 `daily/<date>/interests.yaml` 的结构化 topics |
<table>
<tr>
@ -225,10 +234,10 @@ ReMe 遵循 capture → index → consolidate → recall 的循环。对话和
ReMe 作为本地记忆服务运行，并提供 CLI、HTTP API、MCP server 和 SDK 等多种接入方式。不同 Agent 可以选择适合自身 runtime
的路径，同时共享同一个本地 memory workspace。
| Agent | 推荐接入方式 | 开箱可用能力 |
|------------------------------------------------------|-----------------------------------------------------------------------|--------------------------------------------------------------------|
| **QwenPaw** | 通过 Python SDK 嵌入 ReMe。 | 复用应用自身生命周期和模型配置，同时保持 memory 本地、文件化。 |
| **Claude Code** | 以 MCP service 启动 ReMe，并安装 [plugins/reme](plugins/reme)。 | MCP recall tools、`reme-memory` skill，以及自动记录会话的 Stop hook。 |
| Agent | 推荐接入方式 | 开箱可用能力 |
|------------------------------------------------------|-------------------------------------------------------------------|-----------------------------------------------------------------|
| **QwenPaw** | 通过 Python SDK 嵌入 ReMe。 | 复用应用自身生命周期和模型配置，同时保持 memory 本地、文件化。 |
| **Claude Code** | 以 MCP service 启动 ReMe，并安装 [plugins/reme](plugins/reme)。 | MCP recall tools、`reme-memory` skill，以及自动记录会话的 Stop hook。 |
| **Other CLI-capable agents (OpenClaw/Hermes/Codex)** | 复制或安装 [skills/reme_memory/SKILL.md](skills/reme_memory/SKILL.md)。 | 通过 CLI 搜索/读取/写入记忆，并调用 `auto_memory`、`auto_dream` 和 `proactive`。 |
<p align="center"><b>集成演示</b></p>
@ -264,22 +273,23 @@ ReMe 作为本地记忆服务运行，并提供 CLI、HTTP API、MCP server 和
ReMe 通过 CLI 暴露的统一 job interface 操作 workspace。Agent 通常只需要使用检索、读取、写入、编辑和自动记忆相关命令；更底层的索引、
frontmatter 和文件操作接口主要用于维护、调试或高级集成。完整 job 列表可以运行 `reme help` 查看。
| 命令 | 作用 |
|-----------------------------------------|---------------------------------------------|
| `reme start` | 启动本地 ReMe 服务。 |
| `reme version` / `reme health_check` | 检查包版本和组件状态。 |
| [`reme search`](docs/zh/memory_search.md) | 执行混合记忆检索。 |
| `reme read` / `reme write` / `reme edit` | 检查和维护 Markdown 记忆文件。 |
| `reme auto_memory` | 将对话 messages 转为 daily 记忆卡片；需要 LLM 凭证。 |
| `reme auto_resource` | 将 `resource/` 下的文件解读为 daily 资料卡片；需要 LLM 凭证。 |
| `reme auto_dream` / `reme proactive` | 将 daily 记忆整理为长期 digest，并暴露值得关注的主题。 |
| `reme reindex` | 基于已有文件重建检索和 wikilink 索引。 |
| 命令 | 作用 |
|-------------------------------------------|---------------------------------------------|
| `reme start` | 启动本地 ReMe 服务。 |
| `reme version` / `reme health_check` | 检查包版本和组件状态。 |
| [`reme search`](docs/zh/memory_search.md) | 默认使用 BM25 和 wikilink 检索，启用后增加向量检索。 |
| `reme read` / `reme write` / `reme edit` | 检查和维护 Markdown 记忆文件。 |
| `reme auto_memory` | 将对话 messages 转为 daily 记忆卡片；需要 LLM 凭证。 |
| `reme auto_resource` | 将 `resource/` 下的文件解读为 daily 资料卡片；需要 LLM 凭证。 |
| `reme auto_dream` / `reme proactive` | 将 daily 记忆整理为长期 digest，并暴露值得关注的主题。 |
| `reme reindex` | 基于已有文件重建检索和 wikilink 索引。 |
## 🤝 社区与支持
- **问题反馈与需求**：请先查看 [Open Issues](https://github.com/agentscope-ai/ReMe/issues)；如无相关讨论，可新建 Issue
说明背景、目标行为和影响范围。
- **代码贡献**：改动前建议阅读 [贡献指南](https://docs.agentscope.io/reme/stable/zh/contributing)。架构与扩展方式以源码、schema 和测试为准。
- **代码贡献**：改动前建议阅读 [贡献指南](https://docs.agentscope.io/reme/stable/zh/contributing)。架构与扩展方式以源码、schema
和测试为准。
- **文档贡献**：用户可见文档请提交到[统一文档仓库](https://github.com/agentscope-ai/docs)的 `reme/<version>/{en,zh}/` 目录。
- **提交规范**：建议使用 Conventional Commits，例如 `feat(search): add link expansion option`、
`docs(zh): update quick start`。

2
reme/__init__.py
View file

@ -1,6 +1,6 @@
"""ReMe CLI package."""
__version__ = "0.4.0.9"
__version__ = "0.4.1.0"
from . import config
from . import constants

30
reme/config/default.yaml
View file

@ -599,20 +599,20 @@ components:
default:
backend: regex
as_embedding:
default:
backend: ${EMBEDDING_BACKEND:-openai}
model: ${EMBEDDING_MODEL_NAME:-text-embedding-v4}
dimensions: 1024
credential:
api_key: ${EMBEDDING_API_KEY:-}
base_url: ${EMBEDDING_BASE_URL:-https://dashscope.aliyuncs.com/compatible-mode/v1}
parameters: { }
embedding_store:
default:
backend: local
as_embedding: default
# as_embedding:
# default:
# backend: ${EMBEDDING_BACKEND:-openai}
# model: ${EMBEDDING_MODEL_NAME:-text-embedding-v4}
# dimensions: 1024
# credential:
# api_key: ${EMBEDDING_API_KEY:-}
# base_url: ${EMBEDDING_BASE_URL:-https://dashscope.aliyuncs.com/compatible-mode/v1}
# parameters: { }
#
# embedding_store:
# default:
# backend: local
# as_embedding: default
as_llm:
default:
@ -687,7 +687,7 @@ components:
default:
backend: local
store_name: local
# embedding_store: default
# embedding_store: default
embedding_store: ""
keyword_index: default
file_graph: default